惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

腾讯CDC
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
P
Proofpoint News Feed
D
DataBreaches.Net
D
Docker
云风的 BLOG
云风的 BLOG
大猫的无限游戏
大猫的无限游戏
月光博客
月光博客
J
Java Code Geeks
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
罗磊的独立博客
Martin Fowler
Martin Fowler
U
Unit 42
Engineering at Meta
Engineering at Meta
IT之家
IT之家
Vercel News
Vercel News
B
Blog RSS Feed
人人都是产品经理
人人都是产品经理
博客园 - Franky
博客园 - 【当耐特】
Stack Overflow Blog
Stack Overflow Blog
G
Google Developers Blog
MongoDB | Blog
MongoDB | Blog

math.PR updates on arXiv.org

Visibility in the Boolean Model on Harmonic Manifolds Global estimates on the Brenier map Geodesics and Wandering Exponents in Brochette First-Passage Percolation State-dependent inverse-subordinator time changes of regenerative processes: Excursion structure and multiscale occupation-time limits Randomly twisted transfer operators and singular values statistics Generalized Bessel-Dunkl diffusions An almost sure invariance principle for the Takagi-van der Waerden class functions Central limit theorems for high dimensional lattice polytopes: cosmological polytopes Convergence rate estimates for semigroups and heat kernels associated with resistance forms Second-order Poincaré inequalities and localization on the Poisson space Maximum Probability of Independence in Transitive Matroids On global solutions to the semidiscrete stochastic heat equation The Poisson Tail Conjecture for primes in short intervals A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage Holographic functions and neural networks From Betting to Empirical Bernstein LIL Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Pointwise Generalization in Deep Neural Networks Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process A note on connections between the Föllmer process and the denoising diffusion probabilistic model Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights Propagation of Chaos in Contextual Flow Maps Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Limits of Latent Reuse in Diffusion Models State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Homogenization of the variational principle for discrete ...
Andrew Krieger, Georg Menz, Martin Tassy · 2017-10-31 · via math.PR updates on arXiv.org

We consider homogenization of random surfaces and study the variational principle for graph homomorphisms from subsets of $\mathbb{Z}^m$ into $\mathbb{Z}$, where the underlying uniform measure is perturbed by a random field. Motivated by the theories of random walks in random potentials, we assume that random field is stationary, ergodic, and bounded in $L^1$ . We show that the variational principle holds in probability and that the entropy functional homogenizes, i.e.\ is independent of the values taken by the random field. The main ingredients in the argument are the existence of the quenched surface tension, the equivalence of the quenched and the annealed surface tension, and robustness of the surface tension under change in boundary data. These ingredients are deduced by a combination of a superadditive ergodic theorem and combinatorial results, especially the Kirszbraun theorem.